scRNA-sequencing data and metadata for psoriatic arthritis (PsA) patients that were responders or non-responders to anti-IL17 treatment as well as healthy controls

scDrugPrio: A framework for the analysis of single-cell transcriptomics to address multiple problems in precision medicine in immune-mediated inflammatory diseases

Ineffective drug treatment is a major problem for many patients with immune-mediated inflammatory diseases (IMIDs). Important reasons are the lack of systematic solutions for drug prioritisation and repurposing based on characterisation of the complex and heterogeneous cellular and molecular changes in IMIDs. In our recent work, we propose a computational framework, scDrugPrio, which constructs network models of inflammatory disease based on single-cell RNA sequencing (scRNA-seq) data. scDrugPrio constructs detailed network models of inflammatory diseases that integrate information on cell type-specific expression changes, altered cellular crosstalk and pharmacological properties for the selection and ranking of thousands of drugs.

scDrugPrio was developed using a mouse model of antigen-induced arthritis and validated by improved precision/recall for approved drugs, as well as extensive in vitro, in vivo, and in silico studies of drugs that were predicted, but not approved, for the studied diseases. Next, scDrugPrio was applied to multiple sclerosis, Crohn’s disease, and psoriatic arthritis, further supporting scDrugPrio through prioritisation of relevant and approved drugs. However, in contrast to the mouse model of arthritis, great interindividual cellular and gene expression differences were found in patients with the same diagnosis. Such differences could explain why some patients did or did not respond to treatment. This explanation was supported by the application of scDrugPrio to scRNA-seq data from eleven individual Crohn’s disease patients. The analysis showed great variations in drug predictions between patients, for example,assigning a high rank to anti-TNF treatment in a responder and a low rank in a nonresponder to that treatment. Application to individual patients indicates scDrugPrio’s potential for personalised network-based drug screening on cellulome-, genome-, and drugome-wide scales. For this purpose, we made scDrugPrio into an easy-to-use R package (https://github.com/SDTC-CPMed/scDrugPrio).

PsA patients were recruited from different rheumatology departments from university hospitals belonging to the IMIDC. All PsA patients were diagnosed according to the CASPAR diagnostic criteria for PsA (34) with > 1 year of disease evolution and > 18 years old at the time of recruitment. Exclusion criteria for PsA included the presence of any other form of inflammatory arthritis, rheumatoid factor levels greater than twice the normality threshold or confirmed presence of an inflammatory bowel disease. PBMCs were sampled prior to treatment with anti-TNF and cryopreserved. Treatment response was classified at week 12 according to the EULAR response. For the anti-IL-17 treatment 3 males (2 responders) and 13 females (6 responders) were included. Simultaneously, healthy age- and sex-matched control subjects were recruited from healthy volunteers recruited through the Vall d’Hebron University Hospital in Barcelona (Spain). All the controls were screened for the presence of any autoimmune disorder, as well as for first-degree family occurrence of autoimmune diseases. None were found to be positive. All in all, four males and four females were included as controls.

Patients in the study of PBMC from patients with psoriatic arthritis consented to participate in this study as approved by Hospital Universitari Vall d'Hebron Clinical Research Ethics Committee with reference number 20/0022. Protocols were reviewed and approved by the local institutional review board of each participating centre. This research conformed to the principles of the Helsinki Declaration.

In summary, this data set includes raw scRNA-seq data and metadata of pre-treatment PBMC of psoriatic arthritis patients that did or did not respond to anti-IL17 treatment as well as untreated healthy controls. The RDS file also includes the deep count auto encoder (DCA) denoised scRNA-seq matrix as well as clustering outcomes.